Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model

Environmental Modelling and Software
By: , and 

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Abstract

For decision support, the insights and predictive power of numerical process models can be hampered by insufficient expertise and computational resources required to evaluate system response to new stresses. An alternative is to emulate the process model with a statistical “metamodel.” Built on a dataset of collocated numerical model input and output, a groundwater flow model was emulated using a Bayesian Network, an Artificial neural network, and a Gradient Boosted Regression Tree. The response of interest was surface water depletion expressed as the source of water-to-wells. The results have application for managing allocation of groundwater. Each technique was tuned using cross validation and further evaluated using a held-out dataset. A numerical MODFLOW-USG model of the Lake Michigan Basin, USA, was used for the evaluation. The performance and interpretability of each technique was compared pointing to advantages of each technique. The metamodel can extend to unmodeled areas.

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Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model
Series title Environmental Modelling and Software
DOI 10.1016/j.envsoft.2015.11.023
Volume 77
Year Published 2016
Language English
Publisher Elsevier
Publisher location Oxford
Contributing office(s) Wisconsin Water Science Center
Description 13 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Environmental Modelling & Software
First page 95
Last page 107
Country United States
State Illinois, Indiana, Michigan, Ohio, Wisconsin
Online Only (Y/N) N
Additional Online Files (Y/N) N